Across eight languages, n-gram metrics such as ROUGE correlate less with human ratings in fusional languages than in isolating and agglutinative ones, while the neural metric COMET correlates better, especially in low-resource languages.
Evaluating the Efficacy of Summarization Evaluation across Languages
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abstract
While automatic summarization evaluation methods developed for English are routinely applied to other languages, this is the first attempt to systematically quantify their panlinguistic efficacy. We take a summarization corpus for eight different languages, and manually annotate generated summaries for focus (precision) and coverage (recall). Based on this, we evaluate 19 summarization evaluation metrics, and find that using multilingual BERT within BERTScore performs well across all languages, at a level above that for English.
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Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Across eight languages, n-gram metrics such as ROUGE correlate less with human ratings in fusional languages than in isolating and agglutinative ones, while the neural metric COMET correlates better, especially in low-resource languages.